MacRaild Christopher A, Norton Raymond S
Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, 3052, Australia,
J Biomol NMR. 2014 Mar;58(3):155-63. doi: 10.1007/s10858-014-9813-7. Epub 2014 Jan 21.
Chemical shift prediction has an unappreciated power to guide backbone resonance assignment in cases where protein structure is known. Here we describe Resonance Assignment by chemical Shift Prediction (RASP), a method that exploits this power to derive protein backbone resonance assignments from chemical shift predictions. Robust assignments can be obtained from a minimal set of only the most sensitive triple-resonance experiments, even for spectroscopically challenging proteins. Over a test set of 154 proteins RASP assigns 88 % of residues with an accuracy of 99.7 %, using only information available from HNCO and HNCA spectra. Applied to experimental data from a challenging 34 kDa protein, RASP assigns 90 % of manually assigned residues using only 40 % of the experimental data required for the manual assignment. RASP has the potential to significantly accelerate the backbone assignment process for a wide range of proteins for which structural information is available, including those for which conventional assignment strategies are not feasible.
在已知蛋白质结构的情况下,化学位移预测对于指导主链共振归属具有一种未被充分认识的强大作用。在此,我们描述了通过化学位移预测进行共振归属(RASP)的方法,该方法利用这种强大作用从化学位移预测中推导蛋白质主链共振归属。即使对于光谱分析具有挑战性的蛋白质,仅通过一组最少的、最灵敏的三共振实验就能获得可靠的归属。在154个蛋白质的测试集上,RASP仅使用HNCO和HNCA光谱中的可用信息,就能以99.7%的准确率归属88%的残基。应用于一个具有挑战性的34 kDa蛋白质的实验数据时,RASP仅使用手动归属所需实验数据的40%,就能归属90%的手动归属残基。对于广泛的具有结构信息的蛋白质,包括那些传统归属策略不可行的蛋白质,RASP有潜力显著加速主链归属过程。